In semiconductor device fabrication, defect classification for patterned and unpatterned wafer inspection is the process of parsing scanning inspector defect data based on various defect related parameters including attributes extracted from data acquired during the defect detection process (e.g. patch images, image feature vectors, sensor data streams, and the like) and context attributes derived from external sources (during or after the detection process) such as chip layout. (See, e.g. U.S. Pat. No. 7,676,077 to Kulkarni, et al.; U.S. Pat. No. 7,570,796 to Zaffar, et al.). Such functionality may be carried out by defect classifier modules associated with various scanning defect inspection tools (e.g. bright field patterned wafer inspectors, patterned or unpatterned dark field photon optics wafer inspectors; electron beam optics area scanning inspectors, and the like). Current classifier maintenance methodologies may not measure and leverage historical information and time-dependency trends in classifier performance. Typically, ad hoc snapshots of a classifier's performance may be observed and, where performance is degraded, a need for a change in the classifier may be inferred. Production data is then collected and used to update the classifier.
Current methodologies relying on degraded classifier performance to trigger scrutiny of that performance fails to leverage all information regarding classifier performance as a function of time. As a result, such methodologies do not provide any means of quantifying the changes in behavior of the classifier relative to a baseline. In particular, it does not provide any way to quantify or even to identify whether the classifier performance change is due to instability against process fluctuations reflected in defect properties or is the result of a change in relative populations between defect types. Instead current classifier maintenance methodologies use ad hoc qualitative information rather than cumulated statistical information collected over past production runs.
Such ad hoc metrics lack the ability to quantify the variations in classification performance occurring or predicted to occur from one production wafer to another. Further, such ad hoc metrics do not take into considerations inspector tool hardware and inspector sensitivity considerations. The consequence of these shortcomings is that both under-correction and over-correction can occur in the classification maintenance business process which can put the fab at risk for misreading the significance of defect inspection data.